In this paper, the problem of state estimation, in context both filtering and smoothing, for nonlinear state-space models is considered. Due to nature models, estimation generally intractable as it involves integrals general functions filtered smoothed distributions lack closed-form solutions. As such, common approximate problem. we develop an assumed Gaussian solution based on variational infe...

The author studies the optimization of measurement for n copies of pure states. The asymptotic efficiency of the optimal measurement is calculated in the large deviation sense.

In this contribution we take a look at the computational effort statistics as described by Koza. We transfer the notion from generational genetic programming to tournament-selection (steady-state) GP and show why, in both cases, the measured value of the effort often differs from its theoretical counterpart. It is discussed how systematic estimation errors are introduced by a low number of expe...